Inhalt des Dokuments
Research program in the new funding period (2014-2019)
The scientific goal of this research training group is to apply theoretical and computational tools in order to understand the principles underlying sensory processing and perception. Specifically, we plan to address perception at different scales and different levels of abstraction, aiming at the integration of computation, i.e. the algorithmic level, and neural processing, i.e. its implementation. Therefore, the scientific program:
- addresses sensory processing on the local (neurons and circuits) and global (brain networks and behavior) levels,
- combines experiment-dominated bottom-up (data → computational models → functional concepts) with theory-dominated top-down approaches (functional hypotheses → computational models → testable predictions),
- combines approaches from biophysical modeling, dynamical systems and stochastic processes with methods from machine learning and engineering, providing links between the different levels of abstraction,
- addresses sensory computation in its behavioral context.
Since we are interested in principles of computation that generalize across systems and species, we feel that it is beneficial to study a variety of systems and paradigms, ranging from invasive (electrophysiology, imaging) studies in animals to non-invasive (EEG, fMRI, behavior) studies in humans. Projects thus cover the range from single neuron computation to human psychophysics, because we feel that it is highly important for the success of the research training group that doctoral researchers become familiar with the most important theoretical concepts on all relevant levels of abstraction.
The research program is structured into two pillars:
- Pillar A: Local computations: Neurons, networks & invasive studies.
- Pillar B: Global computation: Brain networks, cognitive aspects & human neuroscience.
Within pillar A, we want to understand
computations implemented by local circuits and the role of the
observed spatiotemporal responses of networks in sensory processing.
Projects address different sensory modalities, and are complemented by
studies of the hippocampus as an example for brain structure which
supports sensory integration and contextual processing.
Pillar B makes the link to perception and human neuroscience.
More conceptually oriented projects will attempt to construct
dynamical models of brain networks underlying sensory computation,
combining for the first time high-resolution, whole brain Diffusion
Tensor Imaging data with fMRI measurements of resting state and evoked
activities
The projects will provide doctoral researchers
with the experience with a broad range of theoretical concepts.
Classical methods from computational neuroscience will be complemented
by less well-known methods from dynamical systems, stochastic
processes, and control. Established methods from the machine learning
and statistical pattern recognition fields will be complemented
by recent developments in Bayesian inference and variational methods,
reinforcement learning, information geometry, subspace methods, or
transfer learning. With the proposed spectrum of projects, mechanisms
underlying sensory computation and perception will be studied at many
different levels. What is more, doctoral researchers will directly
gain hands-on experience in developing theoretical and computational
methods for linking those different levels, for example, the
biophysical and dynamical properties of single neurons with the
effective dynamics of large populations, the representation of
information with the observed neural signals, the computation being
performed with the underlying neural implementation, and quantitative
descriptions of behavior with the underlying computation and the
underlying neural correlates. Hence doctoral researchers will be
exposed to the problem of linking the activity of neurons and networks
to task-dependent performance measures while – at the same time –
being forced to formulate quantitative hypotheses about the ongoing
computation and putting them to test.
Planned research projects
Pillar A: Local computation: Neurons, networks and
invasive studies |
A.1 Impact of HCN channel-mediated conductances on the
excitability of cortical neurons (Schreiber, Vida) |
A.2 Noise correlations and stimulus
coding in visual cortical networks: cats vs. mice
(Obermayer, Nawrot) |
A.3
The Influence of transcranial alternating
current stimulation on neurons and networks (Obermayer,
Lindner) |
A.4 Nonlinear
transient response of a neural network (Lindner, Brecht) |
A.5 Models of optimal stochastic
control in neural systems (Opper, Lindner) |
A.6 Computation of interaural time differences
in the auditory brainstem (Kempter, Lindner) |
A.7 Mechanisms of place-related discharge
patterns in hippocampal CA1 pyramidal cells
subtypes (Vida, Kempter, Brecht) |
A.8 Formation of grid cells in the medial
entorhinal cortex (Kempter, Schreiber, Brecht) |
A.9 Parallel memory phases in a
multi-stage spiking neural network (Nawrot) |
Pillar B: Global computation:
brain networks, cognitive aspects and human
neuroscience |
B.1
Information flow (Müller, Opper, Haynes) |
B.2 Robust spatio-spectral processing and
classification of single-trial EEG (Müller, Blankenburg,
Opper) |
B.3 Topography
of object-representation in human extrastriate visual cortex:
sparsnesses and superposition (Haynes, Schreiber) |
B.4 Decoding sensory working
memory content across modalities (Haynes, Blankenburg) |
B.5 Perceptual learning through
real-time fMRI (Sterzer, Haynes, Obermayer) |
B.6 Computational models of functional brain
networks during human somesthesis (Obermayer,
Blankenburg) |
B.7 From
sensation to sensory working memory representation (Blankenburg,
Opper) |
B.8
Plasticity and transfer in sensory working memory performance and
executive functions (Heinz, Obermayer) |
B.9 Risk-sensitive reward-based learning in
partially observable domains (Obermayer, Opper, Heinz) |
B.10 Contour adaptation and
surface filling-in in visual perception (Maertens,
Obermayer) |
B.11
Extracting depth cues by means of response classication (Maertens,
Obermayer) |
B.12
Sensorimotor learning and integration (Hafner, Nawrot,
Opper) |